Still images were first captured and stored through the use of film cameras. Later developments allowed capture of moving images by taking a sequence of images (frames) and showing them in rapid secession. The reproduction of moving images was enhanced by the use of video technology utilizing magnetic tapes. However, analog formats such as film and magnetic tapes are inherently imperfect since flaws are added in the reproduction of such media.
More recently, both still images and moving (video) images have been stored in a digital format, allowing flawless reproduction of such images. Digitization of images involves segmenting an image into discrete elements. In an example of existing art, the light levels corresponding to each of the discrete elements (pixels) of an image are sensed by a charge coupled device (CCD) producing a numerical value. An image may thus be stored as an array of elements which each have a digital value of the light level for that particular element in the image. Detailed images may be captured and stored by imaging arrays with relatively large numbers of pixels. Additionally, the greater the differential in light levels per pixel, the wider and more detailed the range of colors or grays that may be captured. With a sufficiently high number of pixels and a sufficient level of colors, digital images become indistinguishable from their analog film counterparts.
Detailed digital images, however, require large amounts of storage since they have a large number of pixels. Additionally, each pixel must be capable of storing a large range of numerical values further increasing memory demands. If digital video is considered, even more memory and processing speed are required since digital video systems typically capture or display 30-70 separate images (frames) per second. For example, at 30 frames per second, a modest VGA grade video monitor of 640.times.480 pixel resolution with 24 bits of color requires computing power to process over 27MBytes of raw data per second. At 1024.times.768 pixel resolution, the data processing requirements escalate to over 70Mbytes per second! Current solutions in machine vision and digital video solve the problems through brute computer horsepower and parallel processing techniques with only equivocal success.
The two formidable hurdles that prevent high speed processing of digital video are the retrieval of the data from the sensor and the completion of image processing before retrieval of the next frame. Frame rates, therefore, suffer by the square of the linear frame size as more details and higher resolutions are involved. The higher the pixel count, the greater the problems of retrieval and processing. With such large demands for memory and processing power, it is extremely difficult to produce a cost effective video imaging system without help from image compression. Thus, image compression is a critical aspect of digital video, capture, storage and transmission.
Compression reduces the amount of processing and memory necessary for digital video, which subsequently reduces the information bandwidth required to transmit such digital video signals. Image compression relies on the fact that many pixel elements within a given image have identical numerical values and do not have to be stored separately. In digital video, compression also relies on the fact that the image in a given frame does not change significantly from images in previous frames. To maximize compression effectiveness, only differences between frames are stored whenever possible.
Current techniques for image compression involve software techniques which utilize complex algorithms to find frame differences, reducing the amount of information processed to increase imaging speed. For example, compression schemes today are built on powerful MPEG engines designed specifically for moving pictures. Popular compression algorithms are based on cosine transforms and/or fractal mathematics. Such algorithms may be lossy, in which case data from the original images are lost by approximation. Such "lossy" compression increases speed but sacrifices image accuracy and thus is undesirable for high quality applications such as digital video. Other so called "loss-less" techniques may retain original image detail but sacrifice the frame rate speed necessary for many video applications.
All such software techniques are limited by the physical speed and requirements of the hardware components. To understand the demands placed on these components utilizing present techniques, even if just one pixel changes within an image, a conventional digital video processor must perform the time consuming steps of retrieving a full frame and digitally processing it. Faster hardware components aid in image capture and image processing but the cost of such existing hardware is high and its capabilities are limited.
A new hardware component, that enables substantially faster image capture and processing is the opsistor, which possesses significant advantages for imaging applications compared to a single photodiode in the unbiased or photovoltaic mode. Previously, imaging pixels using a photovoltaic principle were typically based on single photodiode, phototransistor, photodarlington, and the like. These are photosensitive devices with "on", "off" and linear response states. For high speed applications which include machine vision, the inherent speed of such devices was limited by the rate at which they could switch their currents "on" and "off," the limiting factor often being the passive return-to-ground period. Also for an "on" potential to be recognized, the photocurrents had to be at a sufficient amplitude to stand out above background noise. However, the higher the signal current that was needed to generate this recognition, the longer it would take for the photonic device to generate that current level, and the even longer period before the current decay would return to the ground level. These characteristics of previous optoelectronic sensors resulted in relatively slow response speeds in the photovoltaic mode of usually less than 1 MHZ for a standard photodiode, and even slower speeds for more complicated devices such as phototransistors.
Although photovoltaic diodes may be designed to respond to faster frequencies by using special circuitry, the additional components of such circuitry increase the complexity and cost of such devices. Further, the implementation of active circuitry within an imaging pixel decreases the light sensitive area and compromises low light performance.
Thus, a need exists for a rapid image compression circuit that uses a simple and a fast photovoltaic sensor such as a real time spatial optical differentiator type switch, like the opsistor. There is a further need for a simple and efficient image compression system which operates without losing image quality. There also is a need for a simple and efficient image processing circuit which may be integrated with a compression circuit by outputting only differences between frames. There is also a need for an image processing circuit which allows more rapid location of different pixels between frames.